class_docstr: Speech enhancement or separation Frontend model
code: def _align_ref_pre_channels(
      self, ref, pre, ch_dim=2, force_1ch=False
  ):
      if ref is None or pre is None:
          return ref, pre
      # NOTE: input must be a list of time-domain signals
      index = ref[0].new_tensor(self.ref_channel, dtype=torch.long)
  
      # for models like SVoice that output multiple lists of separated signals
      pre_is_multi_list = isinstance(pre[0], (list, tuple))
      pre_dim = pre[0][0].dim() if pre_is_multi_list else pre[0].dim()
  
      if ref[0].dim() > pre_dim:
          # multi-channel reference and single-channel output
          ref = [
              r.index_select(ch_dim, index).squeeze(ch_dim) for r in ref
          ]
      elif ref[0].dim() < pre_dim:
          # single-channel reference and multi-channel output
          if pre_is_multi_list:
              pre = [
                  p.index_select(ch_dim, index).squeeze(ch_dim)
                  for plist in pre
                  for p in plist
              ]
          else:
              pre = [
                  p.index_select(ch_dim, index).squeeze(ch_dim)
                  for p in pre
              ]
      elif ref[0].dim() == pre_dim == 3 and force_1ch:
          # multi-channel reference and output
          ref = [
              r.index_select(ch_dim, index).squeeze(ch_dim) for r in ref
          ]
          if pre_is_multi_list:
              pre = [
                  p.index_select(ch_dim, index).squeeze(ch_dim)
                  for plist in pre
                  for p in plist
              ]
          else:
              pre = [
                  p.index_select(ch_dim, index).squeeze(ch_dim)
                  for p in pre
              ]
      return ref, pre
  
imports: import contextlib
  from typing import Dict, List, Optional, OrderedDict, Tuple
  import numpy
  import torch
  from packaging.version import parse
  from typeguard import check_argument_types
  from espnet2.diar.layers.abs_mask import AbsMask
  from espnet2.enh.decoder.abs_decoder import AbsDecoder
  from espnet2.enh.encoder.abs_encoder import AbsEncoder
  from espnet2.enh.loss.criterions.tf_domain import FrequencyDomainLoss
  from espnet2.enh.loss.criterions.time_domain import TimeDomainLoss
  from espnet2.enh.loss.wrappers.abs_wrapper import AbsLossWrapper
  from espnet2.enh.separator.abs_separator import AbsSeparator
  from espnet2.enh.separator.dan_separator import DANSeparator
  from espnet2.enh.separator.uses_separator import USESSeparator
  from espnet2.torch_utils.device_funcs import force_gatherable
  from espnet2.train.abs_espnet_model import AbsESPnetModel
label_desc: Use image node_id 5 for calling the ESPnetEnhancementModel obj's underlying member method code with example usage: obj._align_ref_pre_channels(ref, pre, ch_dim, force_1ch) and returns: ref,  pre, ref,  pre